Palettized image compression

ABSTRACT

An adaptive entropy coder is coupled with a localized conditioning context to provide efficient compression of images with localized high frequency variations. In one implementation, an arithmetic coder can be used as the adaptive entropy coder. The localized conditioning context includes a basic context region with multiple context pixels that are adjacent the current pixel, each of the context pixels having an image tone. A state is determined for the basic context region based upon a pattern of unique image tones among the context pixels therein. An extended context region that includes the basic context region is used to identify a non-local trend within the context pixels and a corresponding state. A current pixel may be arithmetically encoded according to a previously encoded pixel having the same tone or as a not-in-context element. In one implementation, a not-in-context element may be represented by a tone in a color cache that is arranged as an ordered list of most recent not-in-context values.

FIELD OF THE INVENTION

The present invention relates to data compression and, in particular, tocompression of digital image data.

BACKGROUND AND SUMMARY OF THE INVENTION

A wide range of methods and algorithms are available for compression ofdigital images and, particularly, the image data corresponding to thepixels in a digital image. The different available methods andalgorithms are typically suited to particular types of digital images.An example of such particularized suitability is the JPEG compressionstandard, which is a widely-adopted standard compression algorithm thatis based upon the discrete cosine transform (DCT). JPEG compression andthe DCT are described in, for example, Introduction to Data Compressionby Khalid Sayood, Morgan Kaufmann Publishers, Inc., San Francisco,Calif., 336-348, 1996.

JPEG compression applies the DCT to image data corresponding tosuccessive 8×8 blocks of pixels. The DCT is effective and efficient withregard to linear correlations that arise over each 8×8 block of pixels.Digital images with these characteristics might include images thatcorrespond to photographs or images with relatively large regions ofsimilar color or tone. Images corresponding to photographs typicallyinclude a substantially continuous range of image tones represented bybinary values of 24-bits or more. In many such continuous-tone digitalimages, the variations within the range of image tones are typicallywell accommodated by the DCT in JPEG compression. Images with relativelylarge regions of the same color or tone have no changes in tone overthose regions and may be effectively compressed by JPEG compression, aswell as other compression methods.

It will be appreciated, however, that continuous-tone images are not theonly types of digital images. In some applications, digital images mayinclude a smaller range of discrete image colors or tones (e.g.,represented by 8-bits, or fewer) or localized high frequency variationssuch as text elements or other image discontinuities. These types ofdiscrete-tone images, sometimes called palettized images, arise in avariety of applications including, for example, training or help-screenimages for software that uses widowed or other graphical userinterfaces, shared whiteboards or application sharing, online training,simple animations, etc. The images that arise in such applications areoften inefficiently encoded by JPEG compression due to the spatiallylocalized high frequency and discontinuous features in the images.

The present invention includes an adaptive entropy coder coupled with alocalized conditioning context to provide efficient compression ofdiscrete-tone images with localized high frequency variations. In oneimplementation, an arithmetic coder can be used as the adaptive entropycoder.

The localized conditioning context includes a basic context region withmultiple context pixels that are adjacent the current pixel, each of thecontext pixels having an image tone. A state is determined for the basiccontext region based upon the pattern of unique image tones among thecontext pixels therein. An extended context region that includes thebasic context region is used to identify a non-local trend within thecontext pixels and a corresponding state. The current pixel is thenencoded as having one of the tones in the context or as a not-in-contextelement. In one implementation, a not-in-context element may berepresented by a tone in a color cache that is arranged as an orderedlist of most recent not-in-context values.

Statistics for conditional probabilities are gathered for each contextduring encoding. Many traditional context-based coders choose contextsthat are based on the values present in the surrounding context. Sincethe number of states in the coder grows exponentially with the size ofthe context, traditional context-based coders work well only when thepossible set of values is small (e.g. black and white or binary images),or when the context is very small (one element).

The present invention allows the use of a large context (e.g., up to atleast six elements) with elements that can take on many values. This isperformed by quantizing the huge number of fundamental states (about 300trillion) into a set of meaningful patterns (e.g., 60) that are based onthe number of unique image tones or values. These patterns modelpalettized images well. Quantizing the states into this set ofmeaningful patterns, rather than scalar quantizing the values of theelements, as is traditionally done, greatly increases the efficiencywith which palettized images can be encoded or compressed.

Additional objects and advantages of the present invention will beapparent from the detailed description of the preferred embodimentthereof, which proceeds with reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a computer system that may be used toimplement the present invention.

FIG. 2 is an illustration of a first exemplary palettized image.

FIG. 3 is an illustration of a second exemplary palettized image.

FIG. 4 is a flow diagram of a palettized image compression method forcompressing or encoding data representing palettized images.

FIG. 5 is diagram illustrating a causal context region that is in thevicinity of a current pixel being encoded.

FIG. 6 is a diagram of a predefined encoding pattern or sequence forencoding a palettized image.

FIG. 7 is a flow diagram of a method of determining a state for causalcontext region in the vicinity of a current pixel.

FIG. 8 illustrates a universe of image tone pattern classifications fora basic context region according to the present invention.

FIG. 9 illustrates four possible trend tone patterns of a pair ofsupplemental context pixels.

FIG. 10 is a flow diagram of an entropy coding method employed in oneimplementation of the present invention.

FIGS. 11 and 12 are diagrams illustrating operation of a color cacheemployed in the arithmetic coding method of FIG. 10.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

FIG. 1 illustrates an operating environment for an embodiment of thepresent invention as a computer system 20 with a computer 22 thatcomprises at least one high speed processing unit (CPU) 24 inconjunction with a memory system 26, an input device 28, and an outputdevice 30. These elements are interconnected by at least one busstructure 32.

The illustrated CPU 24 is of familiar design and includes an ALU 34 forperforming computations, a collection of registers 36 for temporarystorage of data and instructions, and a control unit 38 for controllingoperation of the system 20. The CPU 24 may be a processor having any ofa variety of architectures including Alpha from Digital, MIPS from MIPSTechnology, NEC, IDT, Siemens, and others, x86 from Intel and others,including Cyrix, AMD, and Nexgen, and the PowerPC from IBM and Motorola.

The memory system 26 generally includes high-speed main memory 40 in theform of a medium such as random access memory (RAM) and read only memory(ROM) semiconductor devices, and secondary storage 42 in the form oflong term storage mediums such as floppy disks, hard disks, tape,CD-ROM, flash memory, etc. and other devices that store data usingelectrical, magnetic, optical or other recording media. The main memory40 also can include video display memory for displaying images through adisplay device. Those skilled in the art will recognize that the memory26 can comprise a variety of alternative components having a variety ofstorage capacities.

The input and output devices 28 and 30 also are familiar. The inputdevice 28 can comprise a keyboard, a mouse, a physical transducer (e.g.,a microphone), etc. The output device 30 can comprise a display, aprinter, a transducer (e.g., a speaker), etc. Some devices, such as anetwork interface or a modem, can be used as input and/or outputdevices.

As is familiar to those skilled in the art, the computer system 20further includes an operating system and at least one applicationprogram.

The operating system is the set of software which controls the computersystem's operation and the allocation of resources. The applicationprogram is the set of software that performs a task desired by the user,using computer resources made available through the operating system.Both are resident in the illustrated memory system 26.

In accordance with the practices of persons skilled in the art ofcomputer programming, the present invention is described below withreference to acts and symbolic representations of operations that areperformed by computer system 20, unless indicated otherwise. Such actsand operations are sometimes referred to as being computer-executed andmay be associated with the operating system or the application programas appropriate. It will be appreciated that the acts and symbolicallyrepresented operations include the manipulation by the CPU 24 ofelectrical signals representing data bits which causes a resultingtransformation or reduction of the electrical signal representation, andthe maintenance of data bits at memory locations in memory system 26 tothereby reconfigure or otherwise alter the computer system's operation,as well as other processing of signals. The memory locations where databits are maintained are physical locations that have particularelectrical, magnetic, or optical properties corresponding to the databits.

FIGS. 2 and 3 are simplified diagrams illustrating palettized images 46and 48, which are sometimes called discrete-tone images. One way tocharacterize a palettized image is that the number of colors in theimage is small compared to the number of pixels in the image (i.e., theimage size). Another way to characterize a palettized image is that thenumber of colors in the image is represented by N-number or fewer binarybits, with N having a value of about 8. Generally, a palettized image iscontrasted with a continuous tone image, such as a photograph, thatpotentially includes large numbers of colors or tones in comparison tothe number of pixels in the image. As an example, palettized images arecommonly used in various computer user interfaces, including those fordesktop software applications, Internet (e.g., World Wide Web) networksites, etc. It will be appreciated, however, that palettized images canarise in various other computer applications.

FIG. 4 is a flow diagram of a palettized image compression method 50 forcompressing or encoding image data representing palettized images.Palettized image compression method 50 compresses palettized images withhigh efficiency and no loss of information, and so may be referred to asa lossless compression method. Palettized image compression method 50compresses palettized images on a pixel-by-pixel basis.

Process block 52 indicates that a “current” pixel 54 (FIG. 5) isdesignated for encoding. Successive adjacent pixels of a palettizedimage are encoded in a predefined encoding pattern or sequence 56 (FIG.6). In the illustrated implementation, the pixels of a palettized imageare encoded left-to-right across successive horizontal rows of pixels,beginning with a start pixel 58 at the upper left corner of the image.It will be appreciated that other encoding patterns or sequences couldalternatively be applied.

Process block 60 indicates that a state is determined for a causalcontext region 62 (FIG. 5), sometimes referred to as extended causalcontext region 62, that includes a predefined set of previously encodedpixels that are in the vicinity of current pixel 54, as described belowin greater detail. In accordance with the present invention,quantization of causal context region 62 into patterns greatly reducesthe number of possible states in comparison to prior encoding processes,thereby supporting the high efficiency of palettized image compressionmethod 50.

Process block 64 indicates that current pixel 54 is adaptively entropycoded with regard to the predefined set of previously encoded pixelswithin causal context region 62. In one implementation, the adaptiveentropy coding includes arithmetic coding, which is known in the art anddescribed in Introduction to Data Compression by Khalid Sayood, MorganKaufmann Publishers, Inc., San Francisco, Calif., 1996. Other suitableadaptive entropy coding methods include adaptive Huffman codes.

FIG. 7 is a flow diagram of a state determination method 70 fordetermining a state for causal context region 62 in the vicinity ofcurrent pixel 54.

Process block 72 indicates that a basic causal context region 74 (FIG.5) is defined with regard to current pixel 54 to be encoded. Basiccausal context region 74 corresponds to context pixels 76-1, 76-2, 76-3,and 764 in the palettized image that have been previously encoded andare immediately adjacent (i.e., contact) current pixel 54. Theillustrated implementation corresponds to an image in which pixels arearranged in a conventional rectangular array, which results in basiccontext region 74 having only four context pixels 76 that areimmediately adjacent current pixel 54. Also, the arrangement of contextpixels 76 relates to the illustrated left-to-right, top-to-bottomencoding pattern 56. It will be appreciated that a different pixelarrangement and a different encoding pattern could result in a differentbasic context region.

Each pixel has associated with it a value, called an image value, whichcorresponds to the color or tone, or other image characteristics of thepixel. Process block 80 indicates that the image values of contextpixels 76 are compared to determine the pattern of unique image valuesamong the four context pixels, thereby to classify basic context region74 by the pattern of unique image values or tones among the four pixels.For example, basic context region 74 is referred to as one-tone contextif all four context pixels have the same image value. If there are twounique values, basic context region 74 is called a two-tone context, andso on. With only four context pixels 76, the image values withinbasic-context region 74 can correspond to one-, two, three, or four-tonecontexts.

Process block 82 indicates that a tone pattern of basic context region74 is assigned a state according to the arrangement of unique imagevalues or tones among the four context pixels 76. FIG. 8 illustrates theuniverse of fifteen tone patterns classifications for basic contextregion 74.

In particular, a one-tone pattern has only one possible classification90 that is designated as “tone 1, pattern 0” corresponding to allcontext pixels 76 having the same image value or tone (e.g., designated“a”). A two-tone pattern may be any of seven classifications 92-104 thatare designated as patterns 0-6, respectively. A three-tone pattern maybe any of six classifications 106-116 as patterns 0-5, respectively. Afour-tone pattern has only one possible classification 118 that isdesignated as “tone 4, pattern 0.”

This classification of basic context region 74 by the number tones andtheir arrangement is based upon context pixels 76 having equal imagevalues, or not. Other than this equality or inequality of the imagevalues or tones, the classification of basic context region 74 isindependent of the actual image values. The pattern classification isonly concerned with spatial distribution of the pattern, and not thevalues themselves. In contrast, conventional encoding methods baseregional contexts on the actual image values, not the pattern of uniquevalues. Even with a relatively simple color spectrum of 256 colors(i.e., 8-bit colors), a conventional regional context of four pixelswould encompass many millions of possible patterns, rather than the 15patterns shown in FIG. 8. As a result, classification of basic contextregion 74 according to the arrangements of unique image values or tones,rather than actual tones, provides a greatly reduced set of possiblecontext patterns, thereby providing a significant increase in encodingefficiency.

Process block 120 indicates that supplemental context pixels 76-5 and76-6 (FIG. 5) are appended to basic causal context region 74 to completethe extended context region 62 and to detect any non-local trendextending through region 62. Supplemental context pixels 76-5 and 76-6are horizontally- and vertically-aligned with current pixel 54 anddetect horizontal and vertical trends, respectively. As shown in FIG. 2,for example, many palettized images may include image features withhorizontal or vertical boundaries that would correspond to non-localtrends through overall context region 62. A horizontal trend isindicated when context pixel 76-4 has an image value or tone equalingthat of context pixel 76-5. A vertical trend is indicated when contextpixel 76-3 has an image value or tone equaling that of context pixel76-6.

FIG. 9 illustrates the four possible trend tone patterns 124-130 ofsupplemental context pixels 76-5 and 76-6. In trend tone pattern 124,neither of supplemental context pixels 76-5 and 76-6 has the same toneas either of respective context pixels 76-4 and 76-2, therebycorresponding to no trends. In trend tone pattern 126, supplementalcontext pixel 76-5 has the same tone context pixel 76-4, but pixels 76-6and 76-2 differ from each other, thereby corresponding only to ahorizontal trend. In trend tone pattern 128, supplemental context pixel76-6 has the same tone context pixel 76-2, but pixels 76-5 and 76-4differ from each other, thereby corresponding only to a vertical trend.In trend tone pattern 130, both of supplemental context pixels 76-5 and76-6 have the same tones as respective context pixels 76-4 and 76-2,thereby corresponding to both horizontal and vertical trends.

The presence or absence of horizontal or vertical trends classifies eachpattern into another four trend states 124-130. In combination thefifteen possible states or patterns of basic context region 74, the sixcontext pixels 76-1 through 76-6 can represent a total of sixty statesinto which the overall context region can be classified.

Process block 132 indicates that a trend state is assigned to contextregion 62.

FIG. 10 is a flow diagram of an entropy coding method 150 employed inone implementation of the present invention.

Process block 152 indicates that method 150 is initialized that a firstcurrent pixel 54 is designated.

Process block 154 indicates that a context region 62 is classified intoa selected state by, for example, state determination method 70.

Process block 156 indicates that each of the unique values of the pixelsor elements in the context region 62 is indexed, as known in the art ofentropy coding.

Inquiry block 158 represents an inquiry as to whether current element 54has a value equal to that of a previously indexed pixel or element ofthe selected state. Inquiry block 158 proceeds to process block 160whenever current element 54 has a value equal to that of a previouslyindexed pixel or element of the selected state. Inquiry block 158proceeds to process block 162 whenever current element 54 has a valuethat is not equal to that of a previously indexed pixel or element ofthe selected state.

Process block 160 indicates that an entropy code symbol corresponding tothe value of the previously indexed element is written, stored, orotherwise sent.

Process block 164 indicates that an estimated probability mass functionfor each of the indices in the selected state is updated to reflect thesymbol written in process block 160. As is known in the art of entropycoding, a probability mass function is maintained for each symbol beingencoded. This allows the entropy coding to be adaptively trained to thestatistics of each source, and sends shorter symbols for the toneindices that are popular for each state. This adaptation allows thecoder to improve its efficiency by using knowledge of previous patternsin the image to code future similar patterns. Some examples include lineor checkerboard patterns that are quick to learn. Other more complicatedphenomena may include a particular font for text.

Process block 162 indicates that an entropy code symbol is sent ordesignated indicating a not-in-context element.

Inquiry block 170 represents an inquiry as to whether current element 54has a value equal to one maintained in a color cache 172 (FIGS. 11 and12). Color cache 172 maintains an ordered list of the most recently usedvalues that were out of context. The ordered list of color cache 172 isdifferent from a conventional list of the most commonly used values,which does not work as well due to poor local adaptation. Inquiry block170 proceeds to process block 174 whenever current element 54 has avalue equal to one maintained in color cache 172. Inquiry block 170proceeds to process block 176 whenever current element 54 does not havea value equal to one maintained in color cache 172.

Process block 174 indicates that the index for the cache entry isdetermined, and the symbol for the index is written or stored. The indexis determined by sequentially searching the cache from most recentlyused values to least recently used values, ignoring any values that mayhave already appeared in the tones for the context.

It will be appreciated that the logical state of cache 172 is a subsetof elements in the physical cache. Since the value being coded was outof context, it by definition cannot have the same value as any of thetones in the context. Therefore, any elements in cache 172 that have thesame value as any of the tones are not useful, and are removed from thelogical state of cache 172. The remaining values are indexed and theprobability mass function for the indices are estimated and adaptedevery time a value is coded out of context.

Process block 178 indicates that color cache 172 and its estimatedprobability mass function are updated to reflect the symbol written inprocess block 172. As illustrated in FIG. 11, this updating of colorcache 172 includes rotating to the top the value of the symbol beingwritten.

Process block 176 indicates that an entropy code symbol is sent ordesignated indicating a not-in-cache element.

Process block 180 indicates that the index for a color histogram entryis determined, and the symbol for the index is written or stored. Thecolor histogram contains all the possible values for an element andkeeps track of the probabilities of all the out of cache values. Thisdistribution is used to write the symbol for the out of cache value.

Process block 182 indicates that the color histogram and its estimatedprobability mass function are updated to reflect the symbol written inprocess block 180. In addition, the symbol value is placed (pushed) atthe top of color cache 172, and the least recently used value drops(pops) out of the bottom of cache 172 as in a queue (FIG. 12).

Inquiry block 184 represents an inquiry as to whether there is anotherelement or pixel to be coded. Inquiry block 184 proceeds to processblock 186 whenever there is another element or pixel to be coded.Inquiry block 184 proceeds to termination block 188 whenever there isnot another element or pixel to be coded.

Process block 186 indicates that the method advances to the next pixelor element. Process block 186 returns to process block 154.

Having described and illustrated the principles of our invention withreference to an illustrated embodiment, it will be recognized that theillustrated embodiment can be modified in arrangement and detail withoutdeparting from such principles. It should be understood that theprograms, processes, or methods described herein are not related orlimited to any particular type of computer apparatus, unless indicatedotherwise. Various types of general purpose or specialized computerapparatus may be used with or perform operations in accordance with theteachings described herein. Elements of the illustrated embodiment shownin software may be implemented in hardware and vice versa.

In view of the many possible embodiments to which the principles of ourinvention may be applied, it should be recognized that the detailedembodiments are illustrative only and should not be taken as limitingthe scope of our invention. Rather, we claim as our invention all suchembodiments as may come within the scope and spirit of the followingclaims and equivalents thereto.

1. An image encoding method for compressing image data representingplural pixels of plural nonzero image tones, comprising: designating acurrent pixel to be encoded; defining a context region that includesmultiple context pixels that are adjacent the pixel, each of the contextpixels having an image tone; identifying a pattern of unique image tonesamong the context pixels in the context region; assigning a state to thecontext region according to the pattern of unique image tones therein;adaptive entropy coding the current pixel with reference to the state ofthe context region; and identifying a non-local trend within the contextpixels in which identification of each non-local references a trendcontext pixel in addition to the context pixels in which the pattern ofunique tones are identified.
 2. An image encoding method for compressingimage data representing plural pixels of plural nonzero image tones,comprising: designating a current pixel to be encoded; defining acontext region that includes multiple context pixels that are adjacentthe current pixel, each of the context pixels having an image tone;identifying a pattern of unique image tones among the context pixels inthe context region; assigning a state to the context region according tothe pattern of unique image tones therein, wherein the pattern of uniqueimage tones is identified with reference only to context pixels that areimmediately adjacent the current pixel; adaptive entropy coding thecurrent pixel with reference to the state of the context region; andidentifying a non-local trend within the context pixels, identificationof each non-local trend referencing a trend context pixel in addition tothe context pixels in which the pattern of unique tones are identified.3. In a computer readable medium, image encoding software forcompressing image data representing plural pixels of plural nonzeroimage tones, comprising: software for designating a current pixel to beencoded; software for defining a context region that includes multiplecontext pixels that are adjacent the current pixel, each of the contextpixels having an image tone; software for identifying a pattern ofunique image tones among the context pixels in the context region;software for assigning a state to the context region according to thepattern of unique image tones therein; software for adaptive entropycoding the current pixel with reference to the state of the contextregion; and software for identifying a non-local trend within thecontext pixels in which identification of each non-local trendreferences a trend context pixel in addition to the context pixels inwhich the pattern of unique tones are identified.
 4. In a computerreadable medium, image encoding software for compressing image datarepresenting plural pixels of plural nonzero image tones, comprising:software for designating a current pixel to be encoded; software fordefining a context region that includes multiple context pixels that areadjacent the current pixel, each of the context pixels having an imagetone; software for identifying a pattern of unique image tones among thecontext pixels in the context region, wherein the pattern of uniqueimage tones is identified with reference only to context pixels that areimmediately adjacent the current pixel; software for assigning a stateto the context region according to the pattern of unique image tonestherein; software for adaptive entropy coding the current pixel withreference to the state of the context region; and software foridentifying a non-local trend within the context pixels, identificationof each non-local trend referencing a trend context pixel in addition tothe context pixels in which the pattern of unique tones are identified.5. An image encoding method for compressing image data representingplural pixels of plural nonzero image tones, comprising: designating acurrent pixel to be encoded; identifying within a basic context regionthat includes multiple context pixels that are adjacent the currentpixel, each of the context pixels having an image tone, the pattern ofunique image tones among the context pixels in the context region;identifying within an extended context region that includes the basiccontext region a non-local trend within the context pixels; assigning astate according to identifications made within the basic and extendedcontext regions; and adaptive entropy coding the current pixel withreference to the state of the context region.
 6. The method of claim 5in which identifying a non-local trend includes identifying non-localtrends that are in horizontal or vertical alignment with the currentpixel.
 7. The method of claim 5 in which the adaptive entropy codingincludes arithmetic coding.
 8. The method of claim 5 in which thepattern of unique image tones is identified with reference only tocontext pixels that are immediately adjacent the current pixel.
 9. Themethod of claim 5 in which the pattern of tones is identified withreference to only four context pixels.
 10. The method of claim 5 inwhich the adaptive entropy coding includes arithmetic coding in whichthe current pixel may be encoded according to a previously encoded pixelhaving the same tone or as a not-in-context element comprising to a tonein a color cache representing an ordered list of most recentnot-in-context values.
 11. In a computer readable medium, image encodingsoftware for compressing image data representing plural pixels of pluralnonzero image tones, comprising: software for designating a currentpixel to be encoded; software for identifying within a basic contextregion that includes multiple context pixels that are adjacent thecurrent pixel, each of the context pixels having an image tone, apattern of unique image tones among the context pixels in the contextregion; software for identifying within an extended context region thatincludes the basic context region a non-local trend within the contextpixels; software for assigning a state according to identifications madewithin the basic and extended context regions; and software for adaptiveentropy coding the current pixel with reference to the state of thecontext region.
 12. The medium of claim 11 in which the software foridentifying a non-local trend includes software for identifyingnon-local trends that are in horizontal or vertical alignment with thecurrent pixel.
 13. The medium of claim 11 in which the software foradaptive entropy coding includes software for arithmetic coding.
 14. Themedium of claim 11 in which the pattern of unique image tones isidentified with reference only to context pixels that are immediatelyadjacent the current pixel.
 15. The medium of claim 11 in which thepattern of tones is identified with reference to only four contextpixels.
 16. The medium of claim 11 in which the software for adaptiveentropy coding includes software for arithmetic coding in which thecurrent pixel may be encoded according to a previously encoded pixelhaving the same tone or as a not-in-context element corresponding to atone in a color cache representing an ordered list of most recentnot-in-context values.